| dc.description.abstract | Recently, biology has become a data intensive science because
of huge datasets produced by high throughput molecular
biological experiments in diverse areas including the fields of
genomics, transcriptomics, proteomics, and metabolomics.
In molecular biology, the list of components at the genome,
transcriptome, proteome, and metabolome levels is gradually
becoming complete and well-known to scientists. However,
it is not holistically known how these components interact
with each other to grow and maintain and reproduce life at
different phases, in different environments, or with different
challenging conditions. Networks at the molecular level
are constructed to understand and explain processes and
subprocesses of the cell. New tools and algorithms are being
continuously developed for the purpose of handling and
mining big biological data and networks aiming to serve
humanity by developing smart health care systems, new generation
medical tests, drugs, foods, fuel, materials, sensors,
and so on.Overall, this improves the understanding of the cell
or in other words the life as a system.Therefore, the range of
topics under big data and network biology is extensive and the
present special issue is not a comprehensive representation of
the subject. Nonetheless, the articles selected for this special
issue represent versatile topics concerning the title that we
have the pleasure of sharing with the readers.
The review paper “A Glimpse to Background and Characteristics
of Major Molecular Biological Networks” focuses
on biological background and topological properties of
gene regulatory, transcriptional regulatory, protein-protein
interaction, and metabolic and signaling networks. Versatile
information contained in this article is helpful to facilitatea comprehensive understanding and to conceptualize the
foundation of network biology.
The paper titled “METSP: A Maximum-Entropy Classifier
Based Text Mining Tool for Transporter-Substrate
Identification with Semistructured Text” discusses a method
for identifying transporter-substrate pairs by text mining and
applied it to human transporter annotation sentences collected
from UniProt database.The substrates of a transporter
are not only useful for inferring function of the transporter,
but also important in discovery of compound-compound
interactions and reconstruction of metabolic pathways.
Volatile organic compounds (VOCs) play an important
role in chemical ecology specifically in the biological interactions
between organisms and ecosystems.The paper titled
“Development and Mining of a Volatile Organic Compound
Database” discusses creation of a new VOC database by
collecting information scattered in scientific literature and
analyzed the accumulated data to showrelations between biological
functions and chemical structures ofVOCs. This work
also shows that VOC based classification of microorganisms
is consistent with their classification based on pathogenicity.
When inconsistent policies are applied to hospital computer
systems, it can produce enormous problems, such as
stolen information, frequent failures, and loss of the entire
or part of the hospital data. The paper “EMRlog Method
for Computer Security for Electronic Medical Records with
Logic and Data Mining” presents a new method named
EMRlog for computer security systems in hospitals based
on two kinds of policies, that is, directive and implemented
policies. | en_US |
